Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
6 results
Search Results
Item Sink attributes analysis for energy efficient operations of wireless sensor networks under randomly varying temporal and spatial aspects of query generation(Elsevier GmbH, 2015) Kumar, P.; Chaturvedi, A.Rapid advances and the development, compactness and economic viability; in IC technology, network hardware components and associated software have completely change the networking paradigm. The wireless sensor networks (WSNs) have also been not isolated from this unexpected changeover. This paper addresses three principal aspects that have been of interest in the WSN researcher community. These are investigating the suitable cluster formation scheme from some prominent scheme, incorporating the Spatio-temporal aspects of random query generation and subsequently model it using appropriate and extensively used probabilistic distribution functions, and exploring the importance of sink node(s) attributes towards much better energy profile of the WSN, as the energy consumption have been a vital component in deciding the overall network service conditions. The integration of these three aspects led to various case studies, which principally involves, uses of SKM, SFCM, DKM and DFCM as clustering schemes, uniform and Poisson probability mass functions uses to mathematically model the Spatio-temporal dependence of query distribution pattern, and the network surveillance by a single stationary sink, a moveable sink and four stationary sinks. The simulation results of various case studies are analyzed and compared. © 2015 Elsevier GmbH.Item Probabilistic query generation and fuzzy c -means clustering for energy-efficient operation in wireless sensor networks(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2016) Kumar, P.; Chaturvedi, A.Depending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution-based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c-means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed. © Copyright 2016 John Wiley & Sons, Ltd.Item Spatio-temporal probabilistic query generation model and sink attributes for energy-efficient wireless sensor networks(Institution of Engineering and Technology journals@theiet.org, 2016) Kumar, P.; Chaturvedi, A.Proliferation in Micro-Electro-Mechanical-Systems (MEMS) technology along with advancement in distributed computing infrastructure has facilitated the versatile usage and deployment of wireless sensors networks (WSNs) in last one and half decades. WSNs support large number of applications from the civilian and military regimes. Irrespective of these regimes; owing to difficulty associated with battery replenishment, proper energy usage has been at centre stage in WSNs operations. The lifetime of WSNs typically depends upon sensor's energy dissipation pattern, which is non-homogeneous with respect to spatial distribution over any short epochs. The genesis behind this nonhomogeneity is random generation of queries, which owes to application specific spatio-temporal parameters. Importance of spatio-temporal parameters is ubiquitous in WSNs paradigm and uncertainties are inevitable with these parameters, although the degree of uncertainties varies in accordance to applications served. Thus, from network design perspectives, precision involved with spatio-temporal aspects must be given due priority to obtain a mathematical model that maintains a good rapport with realistic query generation process. With these motivations, the study explores: (i) uses of energy-efficient clustering schemes, (ii) incorporation of spatio-temporal parameters uncertainties into probabilistic model of query generation using fuzzy-intervals bound, and (iii) sink attributes to enhance network lifetime. For various network surveillance scenarios; the performance measures average residual energy status and service-time-duration are estimated and analysed. © The Institution of Engineering and Technology 2016.Item Spatial–Temporal Aspects Integrated Probabilistic Intervals Models of Query Generation and Sink Attributes for Energy Efficient WSN(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Kumar, P.; Chaturvedi, A.With advancement in device miniaturization and efficacy of network protocols, in a variety of civilian and military applications, wireless sensor networks (WSNs) architectures find room as viable network paradigm. Invariably, in all these WSN architectures, devising suitable algorithms for the efficient network resources utilization has been a challenging task. In certain events driven scenarios, random arrival pattern of queries generation; their geographical distribution (spatial aspect) and generation rate (temporal aspect) are hard to predict precisely. However, these phenomenons could be appropriately modelled using probabilistic framework while yielding adequate accuracy. Usually, in adopted probabilistic models, the associated control parameters are treated as crisp numbers, which fail to encompass uncertainties that are inevitably associated with the modeled parameters. To include impact of such uncertainties, we propose a modified Poisson PMF expressions in that dependency on spatial and temporal aspects is incorporated based on interval concepts. The paper also validates the dynamic fuzzy c-means algorithm as the most efficient clusters formation scheme. Sink node is an important entity/interface between end users and remotely located sensor nodes. To exploit implications of sink nodes attributes, three different case studies are presented. Wherein, we explore the network surveillance by a single stationary/portable sink and four stationary sinks. Obtained simulation results are analyzed for different scenarios which in principle governed by usage of four distinct clustering schemes and sink(s) attribute driven network surveillance. © 2017, Springer Science+Business Media New York.Item Fuzzy-interval based probabilistic query generation models and fusion strategy for energy efficient wireless sensor networks(Elsevier B.V., 2018) Kumar, P.; Chaturvedi, A.Maintaining the desired service norm in wireless sensor networks (WSNs) over a stipulated lifetime is an important issue as it influences the application or utility of such networks. Inevitably, the impact of uncertainties in query generation process is of significant importance and it rely upon the associated spatio-temporal parameters. Usage of a probabilistic model is investigated to treat the inherent uncertainties. Queries inter-arrival-time-rate (?t) and spatial distribution or density (?a) are incorporated to regulate the parametric Poisson PMF model. Instead of considering crisp values of ?a and ?t that devoid parametric uncertainty, the values are inferred using plane-intervals and fuzzy-intervals. A mathematical framework is presented considering Poisson PMF model with parametric intervals, sink attributes in particular its multiplicity and motion aspects, and the quadrants fusion concept by deliberately modeling the problem in high-dimension space. To validate the proposed approach, uses of four different clustering schemes namely SKM, SFCM, DKM and DFCM are investigated. Combinations of sink attributes and quadrants fusion are carried out as different network scenarios. Obtained simulation results demonstrate the benefit of involving specific sink attributes and enabling quadrants fusion strategy. Based on energy metrics assessment, inference about early estimate of initial energy reserve (IER) or its sufficiency is established. © 2018 Elsevier B.V.Item Design and development of a ground-based kite steer controller for kite-based wind power generation(Springer Science and Business Media Deutschland GmbH, 2025) Castelino, R.V.; Kumar, P.; Kashyap, Y.Kite Power Systems, a class of Airborne Wind Energy Systems (AWES), are capable of harvesting high-altitude wind energy using tethered kites, offering substantial material and efficiency advantages over traditional wind turbines. This paper introduces a novel ground-based Kite Steer Controller (KSC), pivotal for optimizing kite trajectory and power generation. The proposed KSC incorporates a Roll-Pitch-Zone control method, enabling precise steering in figure-of-eight trajectories while maintaining operational efficiency under varying wind conditions, including turbulence. Unlike prior approaches, this study emphasizes a detailed force analysis of control lines, revealing that control forces account for 23% of total aerodynamic forces, and the KSC consumes only 20% of the total power generated during a cycle. Experimental field tests with a 12 m2 Leading Edge Inflatable kite validate the system’s performance, demonstrating robust control capabilities under both steady and turbulent winds. This research advances global efforts in renewable airborne wind energy by presenting a scalable, energy-efficient solution for autonomous kite control, addressing critical challenges in AWES design and deployment. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.